AI-Driven Intelligent Attack Detection for IoT Networks Using Big Data and Machine Learning

With the exponential growth of IoT networks, ensuring robust security has become increasingly critical, as these systems are vulnerable to various cyberattacks. Traditional methods often struggle to handle the massive data generated by IoT devices. This paper introduces an AI-driven, big data approach to intelligent attack detection for IoT networks. Utilizing the NSLKDD dataset, we employed PySpark for preprocessing and chi-square-based feature selection to identify the 15 most significant features, optimizing performance and reducing computational costs. The proposed model, based on XGBoost, achieved outstanding classification results with 98.93% accuracy, and precision, recall, and F1-score approaching 99 %. Comparative analysis against models like Random Forest and LightGBM confirmed its effectiveness, providing a scalable, accurate solution for IoT security.